On-board Fault Diagnosis of HEV Induction Motor Drive at Start-up and During Idle Mode

• Published in: 2007 IEEE Vehicle Power and Propulsion Conference pp. 140 - 147
• Main Authors: Akin, B., Ozturk, S.B., Toliyat, H.A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2007
• Subjects: Condition monitoring; Electric motors; Electrical fault detection; Fault diagnosis; hybrid electric vehicle (HEV); Hybrid electric vehicles; idle; induction motor; Induction motor drives; Induction motors; Mechanical power transmission; On-board diagnostics; real-time fault diagnosis; Rotors; standstill; start-up; Testing
• ISBN: 0780397606; 9780780397606
• ISSN: 1938-8756
• DOI: 10.1109/VPPC.2007.4544114

The integrity of the electric motors in work and passenger vehicles can best be maintained by monitoring its condition frequently on-board the vehicle. In this paper, a signal processing based fault diagnosis scheme for on-board diagnosis of rotor asymmetry at start-up and idle mode is presented. Regular rotor asymmetry tests are done when the motor is running at certain speed under load with stationary current signal assumption. It is quite challenging to obtain these regular test conditions for long enough time during daily vehicle operations. In addition, automobile vibrations cause a nonuniform air-gap motor operation which directly affects the inductances of electric motor and results quite noisy current spectrum. Therefore, examining the condition of electric motor as part of hybrid electric vehicle (HEV) powertrain, conventional rotor fault detection methods become impracticable. The proposed method overcomes the aforementioned problems simply by testing the rotor asymmetry at zero speed. This test can be achieved and repeated during start-up and idle modes. The proposed method can be implemented at no cost basically using the readily available electric motor inverter sensors and microprocessing unit. Induction motor rotor asymmetry fault signatures are experimentally tested online employing the drive embedded master processor (TMS320F2812 DSP) to prove the effectiveness of the proposed method. It is experimentally shown that the proposed method detects the fault harmonics at start-up and standstill to determine the existence and the severity of faults in HEV powertrain.